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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Weight	Objectives
Configuring cloud-native applications for deployment	24%	- Deploying applications to Google Kubernetes Engine 1. Configuring scaling, auto-scaling, and updates 2. Creating and managing workloads and services 3. Managing configurations and secrets - Deploying applications to Cloud Run 1. Deploying from source code or containers 2. Configuring scaling, networking, and security 3. Integrating with event-driven services - Deploying to App Engine or Cloud Functions 1. Managing versions and traffic splitting 2. Configuring serverless deployments
Building and testing applications	23%	- Building applications and containers 1. Using Cloud Build and Artifact Registry 2. Configuring build pipelines and provenance 3. Implementing containerization best practices - Testing applications 1. Using AI-assisted development tools 2. Automating testing in CI/CD workflows 3. Writing unit, integration, and end-to-end tests - Setting up development environments 1. Using Cloud SDK, Cloud Code, Cloud Shell, and Cloud Workstations 2. Emulating Google Cloud services locally
Designing highly scalable, secure, and reliable cloud-native applications	32%	- Designing secure applications 1. Securing data in transit and at rest 2. Applying security best practices and compliance requirements 3. Implementing authentication and authorization mechanisms - Designing for observability and monitoring 1. Planning logging, metrics, and tracing strategies 2. Designing for debugging and troubleshooting - Designing high-performing applications and APIs 1. Selecting appropriate compute and deployment platforms 2. Optimizing for latency and throughput 3. Designing for high availability and fault tolerance
Integrating applications with Google Cloud services	21%	- Integrating with data and storage services 1. Implementing messaging and event streaming 2. Working with relational, NoSQL, and in-memory databases 3. Using object storage and file storage solutions - Implementing application performance monitoring 1. Using operations suite tools 2. Instrumenting code for metrics and traces - Consuming Google Cloud APIs and services 1. Using client libraries, REST, and gRPC 2. Integrating with AI/ML and generative AI services 3. Enabling and managing APIs

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Google Certified Professional - Cloud Developer Sample Questions (Q350-Q355):

NEW QUESTION # 350
Your company stores their source code in a Cloud Source Repositories repository. Your company wants to build and test their code on each source code commit to the repository and requires a solution that is managed and has minimal operations overhead.
Which method should they use?

A. Use Cloud Build with a trigger configured for each source code commit.
B. Use Jenkins deployed via the Google Cloud Platform Marketplace, configured to watch for source code commits.
C. Use a Compute Engine virtual machine instance with an open source continuous integration tool, configured to watch for source code commits.
D. Use a source code commit trigger to push a message to a Cloud Pub/Sub topic that triggers an App Engine service to build the source code.

Answer: A

NEW QUESTION # 351
Your team uses Cloud Storage for a video and image application that was recently migrated to Google Cloud. Following a viral surge, users are reporting application instability, coinciding with a
10x increase in HTTP 429 error codes from Cloud Storage APIs. You need to resolve the errors and establish a long-term solution. You want to ensure that the application remains stable if the load increases again in the future. What should you do?

A. Compress the video and images files to reduce their size, and minimize storage costs and bandwidth usage. Implement a custom throttling mechanism in the application that limits the number of concurrent API calls.
B. Implement a retry strategy with exponential backoff for requests that encounter HTTP 429 errors.
C. Migrate all image and video data to Firestore. Replace the Cloud Storage APIs in the application code with the new Firestore database.
D. Optimize the application code to reduce unnecessary calls to Cloud Storage APIs to prevent HTTP 429 errors.

Answer: B

Explanation:
https://blog.hubspot.com/website/http-error-429

NEW QUESTION # 352
Your team has created an application that is hosted on a Google Kubernetes Engine (GKE) cluster You need to connect the application to a legacy REST service that is deployed in two GKE clusters in two different regions. You want to connect your application to the legacy service in a way that is resilient and requires the fewest number of steps You also want to be able to run probe-based health checks on the legacy service on a separate port How should you set up the connection?

A. Use Traffic Director with a sidecar proxy to connect the application to the service.
B. Configure the legacy service's firewall to allow health checks originating from the Traffic Director control plane.
C. Configure the legacy service's firewall to allow health checks originating from the application.
D. Use a proxyless Traffic Director configuration to connect the application to the service.
E. Configure the legacy service's firewall to allow health checks originating from the proxy.

Answer: A,E

Explanation:
https://cloud.google.com/traffic-director/docs/advanced-setup#routing-rule-maps https://cloud.google.com/traffic-director/docs/advanced-setup A) Using Traffic Director with a sidecar proxy can provide resilience for your application by allowing for failover to the secondary region in the event of an outage. The sidecar proxy can route traffic to the legacy service in either of the two GKE clusters, ensuring high availability. C. Configuring the legacy service's firewall to allow health checks originating from the proxy allows the proxy to periodically check the health of the legacy service and ensure that it is functioning properly. This helps to ensure that traffic is only routed to healthy instances of the legacy service, further improving the resilience of the setup.

NEW QUESTION # 353
You are currently pushing container images to Artifact Registry and deploying a containerized microservices application to GKE. After deploying the application, you notice that the services do not behave as expected. You use the kubectl get pods command to inspect the state of the application Pods, and discover that one of the Pods has a state of CrashLoopBackoff. How should you troubleshoot the Pod?

A. Execute the gcloud projects get-iam-policy PROJECT_ID command where the PROJECT_ID parameter is the name of the project where your Artifact Registry resides. Inspect the IAM bindings of the node pool's service account. Validate if the service account has the roles/artifactregistry.reader role.
B. Run the kubectl logs POD_NAME command where the POD_NAME parameter is the name of the problematic Pod. Analyze the logs of the Pod from previous runs to determine the root cause of failed start attempts of the Pod.
C. Connect to the problematic Pod by running the kubectl exec -it POD_NAME - /bin/bash command where the POD_NAME parameter is the name of the problematic Pod. Inspect the logs in the
/var/log/messages folder to determine the root cause.
D. In the Google Cloud console, navigate to Cloud Logging in the project of the cluster's VPC. Enter a filter to show denied egress traffic to the Private Google Access CIDR range. Validate if egress traffic is denied from your GKE cluster to the Private Google Access CIDR range.

Answer: B

Explanation:
https://komodor.com/learn/how-to-fix-crashloopbackoff-kubernetes-error/

NEW QUESTION # 354
Your team is writing a backend application to implement the business logic for an interactive voice response (IVR) system that will support a payroll application. The IVR system has the following technical characteristics:
* Each customer phone call is associated with a unique IVR session.
* The IVR system creates a separate persistent gRPC connection to the backend for each session.
* If the connection is interrupted, the IVR system establishes a new connection, causing a slight latency for that call.
You need to determine which compute environment should be used to deploy the backend application. Using current call data, you determine that:
* Call duration ranges from 1 to 30 minutes.
* Calls are typically made during business hours.
* There are significant spikes of calls around certain known dates (e.g., pay days), or when large payroll changes occur.
You want to minimize cost, effort, and operational overhead. Where should you deploy the backend application?

A. Google Kubernetes Engine cluster in Standard mode
B. Cloud Run
C. Cloud Functions
D. Compute Engine

Answer: B

Explanation:
This page shows Cloud Run-specific details for developers who want to use gRPC to connect a Cloud Run service with other services, for example, to provide simple, high performance communication between internal microservices. You can use all gRPC types, streaming or unary, with Cloud Run.
Possible use cases include:
Communication between internal microservices.
High loads of data (gRPC uses protocol buffers, which are up to seven times faster than REST calls).
Only a simple service definition is needed, you don't want to write a full client library.
Use streaming gRPCs in your gRPC server to build more responsive applications and APIs.
https://cloud.google.com/run/docs/tutorials/secure-services#:~:text=The%20backend%20service%20is%20priva

NEW QUESTION # 355
......

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